Real-Time Intelligence from IoT Analytics

Traditionally businesses uses reports from their data warehouse or depends heavily on analysis from Excel spreadsheets to optimize their operations. Most of organizations start moving to data warehouse in 1990s but data analysis from it is often very slow & took hours or days to complete. To overcome this drawback of data warehouse, Internet of Things come to rescue of tech  industry. IOT Analytics enables businesses to make faster & better business decisions. But to get business insights from IOT Analytics, organizations needs to overcome challenges of data integration, data quality & business intelligence.

Data Integration is commonly cited as one of the costliest and most complicated aspects of IOT projects. IT Systems in an IT architecture might be unable to share data or they might transmit data that an analytics systems can’t parse. As per Gartner estimates that through 2018, half of cost of implementing IOT Solutions will be spent on integration. Average cost per project of integrating device, data & systems is about $8 million per project. As per McKinsey , 40 percent of the value of the IOT will be spend on interoperability. Though challenging but if the data integration is done properly it can bring enormous value. An example could be of Indian Oil. The Company was able to see real-time data on consumption of liquid petroleum gas. The Company collected data from 43,000 IOT –enabled customer touch points in oil and gas refining, distribution and retailing , then integrating that data with customer accounts and applied analytics , which results in saving of over $475 million a year.

Another challenge for IOT Analytics is data quality. Bad data not only affect sales, lower customer satisfaction, and result in bad decisions or delayed decisions. In extreme case like inaccurate bills, wrong data can provoke angry lawsuits. Big data heightens the challenge of data quality. The amount of data generated by industrial equipment – second-by-second readings of temperature, vibration and pressure – can be on the scale of terabytes per day.According to SAP, the business benefits of data quality include a 15 to 20 percent increase in revenue as well as reduced operating costs. A data quality tool can capture real-time alerts on business-critical data discrepancies, and quickly address data issues before they impact performance.Data from a relational database is usually structured, but machine data often arrives as a series of numbers and letters that make no sense to a business manager. Machines may also emit so much data that an analytics system can’t keep pace and rules may need to be applied so that only relevant data is sent forward.Social media data may be semi-structured, unable to be understood by a marketing manager. Often these data sources also need to be enriched with vital information containing customer account IDs.

Lastly the role of business intelligence is very important for successful implementation of IOT Analytics. Organizations need to deliver the right data at the right time to decision-makers. In that respect, finding data can be as important as delivering it. Applying predictive scoring to data in motion, for instance, can help identify crucial data that gets selected for further analysis and delivery. Consider a manufacturer that needs to monitor how equipment performs to optimize production and prevent waste or downtime. A business manager does not need to see millisecond readings of temperature, vibration, or pressure on a piece of manufacturing equipment; he or she only needs an alert, based on predictive scoring, whether something might be wrong.

The road from data integration to actionable analytics requires companies to overcome all three of IOT Analytics challenges.